Systems and methods for fly-by-wire reversionary flight control

ABSTRACT

Some aspects relate to systems and methods for fly-by-wire reversionary flight control including a pilot control, a plurality of sensors configured to: sense control data associated with the pilot control, and transmit the control data, a first actuator communicative with the plurality of sensors configured to receive the control data, determine a first command datum as a function of the control data and a distributed control algorithm, and actuate a first control element according to the first command datum.

RELATED APPLICATION DATA

This application is a continuation of Non-provisional application Ser.No. 17/320,309 filed on May 14, 2021 and entitled “SYSTEMS AND METHODSFOR FLY-BY-WIRE REVERSIONARY FLIGHT CONTROL,” the entirety of which isincorporated herein by reference.

FIELD OF THE INVENTION

The present invention generally relates to the field of computerizedvehicles controls and navigation. In particular, the present inventionis directed to systems and methods for fly-by-wire reversionary flightcontrol.

BACKGROUND

Fly-by-wire, where electronics mediate flight controls, is used morefrequently in modern aviation. Commonly, provisions exist for flightcontrols on conventional fixed-wing aircraft to experience a reversionback to manual controls in case of an emergency affecting function of afly-by-wire flight controller. Fixed-wing aircraft have control systemswhich can be understood and controlled by a human pilot. Therefore,reversion to a manual control system grants a pilot an opportunity tocontrol her aircraft, albeit in an impaired state, when a flightcontroller experiences a malfunction. Conversely, many vertical take-offand landing (VTOL) aircraft, such as quadcopters and the like, requirethe use of flight control systems which must be implemented by way of acomputer and cannot be controlled directly by a human pilot. Failure offlight control systems in these aircraft does not permit a flightcontrol reversion allowing the pilot to control her aircraft.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for fly-by-wire reversionary flight control, thesystem comprising an electric aircraft, wherein the electric aircraft isconfigured to include a first control element, and a first actuatorcoupled to the first control element, wherein the first actuator isconfigured to receive a control data, determine a first command datum asa function of the control data and a distributed control algorithm, andactuate a first control element as a function of the first commanddatum.

In another aspect a method of fly-by-wire reversionary flight controlfor an electric aircraft, the method comprising receiving, at a firstactuator coupled to a first control element, a control data,determining, at the first actuator coupled to the first control element,a first command datum as a function of the control data and adistributed control algorithm, and actuating, at the first actuatorcoupled to the first control element, a first control element as afunction of the first command datum.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary system forfly-by-wire reversionary flight control;

FIG. 2 is a block diagram illustrating an exemplary data filteringalgorithm;

FIG. 3 is a block diagram illustrating an exemplary data filteringalgorithm;

FIG. 4 is a schematic illustrating an exemplary electronic verticaltake-off and landing aircraft;

FIG. 5 is a block diagram illustrating an exemplary flight controller;

FIG. 6 is a block diagram illustrating an exemplary machine-learningprocess;

FIG. 7 is a flow diagram illustrating an exemplary method of fly-by-wirereversionary flight control; and

FIG. 8 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for fly-by-wire flight control reversion. In anembodiment, fly-by-wire flight control reversion may be employed in anelectric vertical take-off and landing (VTOL) aircraft. Flight controlreversion is currently available for many fly-by-wire fixed wingaircraft, but not on VTOL aircraft. This failing must be addressed inorder for VTOL aircraft to satisfy the exceptional safety standards thepublic has come to expect in air travel.

Aspects of the present disclosure can be used to ensure pilot flightcontrol when a flight controller experiences a malfunction. Aspects ofthe present disclosure can also be used to allow a pilot more directcontrol of flight components, such as actuators and propulsors, withoutmediation of a flight controller. This is so, at least in part, becauseflight controller failure poses a risk to air travel, which isespecially dire with VTOL aircraft.

Aspects of the present disclosure allow for fly-by-wire reversion offlight controls in case of a malfunction of a flight controller.Exemplary embodiments illustrating aspects of the present disclosure aredescribed below in the context of several specific examples.

Referring now to FIG. 1, exemplary system 100 for fly-by-wire reversionflight control configured for use in electric aircraft is illustrated inblock diagram form. System 100 includes a plurality of sensors 104.Plurality of sensors 104 is communicatively coupled to at least a pilotcontrol 108. Communicative coupling may include two or more componentsbeing electrically, or otherwise connected and configured to transmitand receive signals from one another. Signals may include electrical,electromagnetic, visual, audio, radio waves, or another undisclosedsignal type alone or in combination. Plurality of sensors 104communicatively coupled to at least a pilot control 108 may include atleast a sensor disposed on, near, around or within at least pilotcontrol 108. Plurality of sensors 104 may include a motion sensor.“Motion sensor,” in this disclosure is a device or component configuredto detect physical movement of an object or grouping of objects. One ofordinary skill in the art would appreciate, after reviewing the entiretyof this disclosure, that motion may include a plurality of typesincluding but not limited to: spinning, rotating, oscillating, gyrating,jumping, sliding, reciprocating, or the like. Non-limiting exemplarymotion sensors may include magnetic encoders, quadrature sensors,optical encoders, Hall effect sensors, and the like. Plurality ofsensors 104 may include any of torque sensor, gyroscope, accelerometer,torque sensor, magnetometer, inertial measurement unit (IMU), pressuresensor, force sensor, proximity sensor, displacement sensor, vibrationsensor, among others. Plurality of sensors 104 may include a sensorsuite which may include a plurality of sensors that may sense similar ordissimilar phenomenon. For example, in a non-limiting embodiment, sensorsuite may include a plurality of accelerometers, a mixture ofaccelerometers and gyroscopes, or a mixture of an accelerometer,gyroscope, and torque sensor. Plurality of sensors 104 may include aplurality of sensors in a form of individual sensors or a sensor suiteworking in tandem or individually. A sensor suite may include aplurality of independent sensors, as described herein, where any numberof the sensors may be used to detect any number of physical orelectrical quantities associated with a pilot control 108. Independentsensors may include separate sensors measuring physical or electricalquantities that may be powered by and/or in communication with circuitsindependently, where each sensor may output to a common circuit. In anembodiment, use of a plurality of independent sensors may result inredundancy configured to employ more than one sensor that measuressubstantially similar phenomenon. Redundant sensors may be of a sametype or a combination of different types; so should one sensor fail, theredundant sensors may continue to sense a phenomenon. A plurality ofsensors 104 may be configured to sense a pilot input 112 from at leastpilot control 108. As used in this disclosure, a “pilot control” is anysystem for inputting control data. At least pilot control 108 mayinclude a throttle lever, inceptor stick, collective pitch control,steering wheel, brake pedals, pedal controls, toggles, joystick, and thelike. One of ordinary skill in the art, upon reading the entirety ofthis disclosure would appreciate a variety of pilot input controls thatmay be present in an electric aircraft consistent with the presentdisclosure. Inceptor stick may be consistent with disclosure of aninceptor stick in U.S. patent application Ser. No. 17/001,845 entitled“A HOVER AND THRUST CONTROL ASSEMBLY FOR DUAL-MODE AIRCRAFT,” which isincorporated herein by reference in its entirety. Collective pitchcontrol may be consistent with disclosure of a collective pitch controlin U.S. patent application Ser. No. 16/829,206 entitled “HOVER ANDTHRUST CONTROL ASSEMBLY FOR DUAL-MODE AIRCRAFT,” which is incorporatedherein by reference in its entirety. At least pilot control 108 may bephysically located in a cockpit of an aircraft or remotely locatedoutside of the aircraft in another location communicatively coupled toat least a portion of the aircraft. “Communicatively coupled,” as usedin this disclosure, is a process whereby one device, component, orcircuit is able to receive data from and/or transmit data to anotherdevice, component, or circuit; communicative coupling may be performedby wired or wireless electronic communication, either directly or by wayof one or more intervening devices or components. In an embodiment,communicative coupling includes electrically coupling an output of onedevice, component, or circuit to an input of another device, component,or circuit. Communicative coupling may be performed via a bus or otherfacility for intercommunication between elements of a computing device.Communicative coupling may include indirect connections via “wireless”connection, low power wide area network, radio communication, opticalcommunication, magnetic, capacitive, or optical coupling, or the like.At least a pilot control 108 may include buttons, switches, or otherbinary inputs. Additionally or alternatively, at least a pilot control108 may include digital controls or analog controls. At least a pilotcontrol 108 may be configured to receive pilot input 112. Pilot input112 may include a physical manipulation of a control, such as withoutlimitation a pilot using a hand and arm to push or pull a lever, or apilot using a finger to manipulate a switch. Pilot input 112 may includea voice command by a pilot to a microphone and computing systemconsistent with the entirety of this disclosure.

With continued reference to FIG. 1, plurality of sensors 104 may beconfigured to sense, for example as a function of pilot input 112,control data 116. “Control data,” as used in this disclosure, is aplurality of signals, each signal representing at least an element ofdata correlated to a desired control of any element of an aircraft. Forexample, in some cases, control data may include a plurality of signals,where at least a signal of the plurality of signals represents at leastan element of data correlated to a pilot input 112. In some cases,control data may include a pilot signal as described in greater detailbelow, for example in reference to FIG. 6. Alternatively oradditionally, in some embodiments, control data may include a pluralityof signals, where at least a signal of the plurality of signalsrepresents at least an element of data correlated to a remote input.Remote inputs may be received from at least a remote device as describedin greater detail below, for example in reference to FIG. 6. Furtherstill, in some embodiments, control data may include a plurality ofsignals, where at least a signal of the plurality of signals representsan automated input. Automated inputs may be provided by a flightcontroller operating in an autonomous mode or autonomous function asdescribed in greater detail below, for example in reference to FIG. 6.For example without limitation, control data 116 may represent a desiredchange in aircraft conditions or flight control parameters. A “datum”,for the purposes of this disclosure, refers to at least an element ofdata. In some cases, a datum may identify a pilot input 112. At least apilot control 108 may be communicatively coupled to any other componentpresented in system 100; communicative coupling may include redundantconnections, for instance which may be configured to safeguard againstsingle-point failure. Pilot input 112 may indicate a pilot's desire tochange heading or trim of an electric aircraft. Pilot input 112 mayindicate a pilot's desire to change an aircraft's pitch, roll, yaw, orthrottle. “Pitch,” as used in this disclosure is an aircraft's angle ofattack; the angle of attack may be approximated as a difference betweenthe aircraft's attitude and the aircraft's horizontal flight trajectory.For example, an aircraft pitches “up” when its nose is angled upwardcompared to horizontal flight, for example while in a climb maneuver. Inanother example, the aircraft pitches “down”, when its nose is angleddownward compared to horizontal flight, for example while in a divemaneuver. “Roll,” as used in this disclosure, refers to an aircraft'sposition about its longitudinal axis, running from its tail to its nose.“Yaw,” as described in this disclosure, is an aircraft's turn angle,when an aircraft rotates about an imaginary vertical axis intersectingthe center of the earth and a fuselage of the aircraft. “Throttle,” asused in this disclosure, is an amount of thrust from a propulsor. Pilotinput 112, when referring to throttle, may refer to a pilot's desire toincrease or decrease thrust produced by at least a propulsor. Controldata 116 may include a plurality of electrical signals. Electricalsignals may include analog signals, digital signals, periodic oraperiodic signals, step signals, unit impulse signals, unit rampsignals, unit parabolic signals, one or more signum functions,exponential signals, rectangular signals, triangular signals, sinusoidalsignals, one or more sine functions, pulse width modulated signals, orthe like. Plurality of sensors 104 may include circuitry, computingdevices, electronic components or a combination thereof that translatespilot input 112 into control data 116 configured to be transmitted toanother electronic component.

With continued reference to FIG. 1, system 100 may include a flightcontroller 120. Flight controller 120 may be communicatively coupled toat least a pilot control 108 and plurality of sensors 104. Flightcontroller 120 may include any flight controller described in thisdisclosure, for example flight controller detailed description below.Communicative coupling may be consistent with any embodiment ofcommunicative coupling as described herein. According to someembodiments, flight controller 120 may be configured to perform a votingalgorithm. Flight controller, in some instances, may be a component orgrouping of components that controls one or more actuators of anaircraft by taking in signals from a pilot and/or remote device andoutputting signals to the one or more actuators. In some cases, one moreactuators may include at least a propulsor, at least a control elements,and the like. Flight controller 120 may mix, refine, adjust, redirect,combine, separate, or perform other types of signal operations totranslate pilot desired trajectory into aircraft maneuvers. Flightcontroller may condition signals such that they can be sent and receivedby various components throughout an aircraft.

With continued reference to FIG. 1, flight controller 120 may includeand/or communicate with any computing device, including withoutlimitation a microcontroller, microprocessor, digital signal processor(DSP) and/or system on a chip (SoC). Flight controller may be programmedto operate electronic aircraft to perform at least a flight maneuver; atleast a flight maneuver may include takeoff, landing, stability controlmaneuvers, emergency response maneuvers, regulation of altitude, roll,pitch, yaw, speed, acceleration, or the like during any phase of flight.At least a flight maneuver may include a flight plan or sequence ofmaneuvers to be performed during a flight plan. Flight controller may bedesigned and configured to operate aircraft via fly-by-wire. Flightcontroller may be communicatively coupled to at least a propulsor. Insome embodiments, flight controller 120 may be communicatively coupledto each propulsor; so, the flight controller may transmit signals toeach propulsor and each propulsor is configured to modify an aspect ofpropulsor behavior in response to the signals. As a non-limitingexample, flight controller 120 may transmit signals to a propulsor viaan electrical circuit connecting flight controller to the propulsor; theelectrical circuit may include a direct conductive path from the flightcontroller to the propulsor or may include an isolated coupling such asan optical or inductive coupling. According to some embodiments, atleast a propulsor may be controlled by an actuator.

Within continued reference to FIG. 1, system 100 may additionallyinclude a first actuator 124. According to some embodiments a firstactuator 124 may be communicatively coupled to flight controller 120 anda control element of the aircraft. As used in this disclosure, an“actuator” is system that affects, for example affects a movement of, aflight component, control element, or any other physical component of anaircraft. An actuator may include an electro-mechanical or anopto-mechanical system. An actuator may include a computing device orplurality of computing devices consistent with the entirety of thisdisclosure. An actuator may be designed and/or configured to perform anymethod, method step, or sequence of method steps in any embodimentdescribed in this disclosure, in any order and with any degree ofrepetition. For instance, an actuator may be configured to perform asingle step or sequence repeatedly until a desired or commanded outcomeis achieved; repetition of a step or a sequence of steps may beperformed iteratively and/or recursively using outputs of previousrepetitions as inputs to subsequent repetitions, aggregating inputsand/or outputs of repetitions to produce an aggregate result, reductionor decrement of one or more variables such as global variables, and/ordivision of a larger processing task into a set of iteratively addressedsmaller processing tasks. An actuator may perform any step or sequenceof steps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Within continued reference to FIG. 1, an actuator may include a pistonand cylinder system configured to utilize hydraulic pressure to extendand retract a piston coupled to at least a portion of aircraft. Anactuator may include a stepper motor or server motor configured toutilize electrical energy into movement of a rotor. An actuator mayinclude a system of gears coupled to a motor configured to convertelectrical energy into kinetic energy and mechanical movement through asystem of gears. An actuator may include components, processors,computing devices, or the like configured to detect a control element.An actuator may be configured to move at least a portion of aircraft asa function of command datum. As used in this disclosure a “commanddatum” is any element of information that indicates a purposeful stateof change of state of a control element, flight component, or actuator.A command datum may indicate a desired change in aircraft heading orthrust. According to some embodiments, a command datum may be derivedfrom pilot input 112 and control data 116, for example withoutlimitation by performing a control algorithm. That is to say commanddatum is derived from a pilot input, for example without limitation in aform of moving an inceptor stick; and the command datum may be receivedby at least an actuator that in turn, actuates according to the commanddatum, for instance thereby moving at least a portion of aircraft, toaccomplish the pilot's desired maneuver.

With continued reference to FIG. 1, in some embodiments, an actuator maybe configured to move control surfaces and/or control elements ofaircraft in one or both of its two main modes of locomotion, or adjustthrust produced at any propulsor. A “control element,” as described inthis disclosure is any element that can interact with forces to move anaircraft. Non-limiting exemplary control elements include controlsurfaces, hot-air balloons, rockets, and jets. A “control surface,” asdescribed in this disclosure, is any form of a mechanical linkage with asurface area that interacts with forces to move an aircraft. A controlsurface may include, as a non-limiting example, ailerons, flaps, leadingedge flaps, rudders, elevators, spoilers, slats, blades, stabilizers,stabilators, airfoils, a combination thereof, propulsors, or any othermechanical surface are used to control an aircraft in a fluid medium.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various mechanical linkages that may beused as a control surface, as used and described in this disclosure.

With continued reference to FIG. 1, flight controller 120 maycommunicate with an actuator using wireless communication, such aswithout limitation communication performed using electromagneticradiation including optical and/or radio communication, or communicationvia magnetic or capacitive coupling. In some cases, flight controller120 may be fully incorporated in an aircraft and may be a remotelyaccessible by a remote device that may operate the aircraft remotely viawireless or radio signals. Alternatively or additionally, a computingdevice in aircraft may be configured to perform some steps or actionsdescribed herein while a remote device is configured to perform othersteps. Persons skilled in the art will be aware, after reviewing theentirety of this disclosure, of many different forms and protocols ofcommunication that may be used to communicatively couple flightcontroller to one or more actuators.

With continued reference to FIG. 1, a system for fly-by-wirereversionary flight control 100 may include a first actuator 124communicative with plurality of sensors 104. In some cases, firstactuator 124 may be configured to receive control data 116, for examplewithout limitation, directly from plurality of sensors 104. In somecases, first actuator 124 may determine a first command datum as afunction of control data 116 and a distributed control algorithm. Firstcommand datum may include any command datum described in thisdisclosure. First actuator 124 may actuate a first control element 128according to first control datum. First control element 128 may includeany control element described in this disclosure. For instance, in someembodiments, first control element 128 may include one or morecomponents of a propulsor.

Within continued reference to FIG. 1, according to some embodiments, anactuator may utilize a distributed control algorithm in order todetermine a command datum. As used in this disclosure, a “distributedcontrol algorithm” is a control algorithm that is performed not on aflight controller, but on at least an actuator. A distributed controlalgorithm may deviate in performance from control methods employed by aflight controller in a number of ways. For example, in some cases, adistributed control algorithm may not allow for automated control (i.e.,auto-pilot) and rely solely on pilot control 108; additionally oralternatively, in some cases, the distributed algorithm may not containcalibration, for instance of a plurality of actuators, which is commonlyperformed by a flight controller 120. In some cases, distributed controlalgorithm may include filtering control data 116; filtering the controldata 116 may include voting. Control data 116 voting is described indetail below. In some cases, distributed control algorithm may includefinding an average of two or more control datums of control data 116.For instance, finding an average of two or more control datums may beused to find a collective datum. In some cases, distributed controlalgorithm may include finding a difference between two or more controldatums of the control data. For instance, finding a difference betweentwo or more control datums may be used to find a stick datum. Accordingto some embodiments, distributed control algorithm may be contingentlyoperative. For instance, in some cases, an actuator may determine acommand datum only when the actuator is unable to receive a commanddatum from a flight controller 120.

Still referring to FIG. 1, in some embodiments, a flight controller 120may be communicative with plurality of sensors 104 and a first actuator124; and the flight controller 120 may be configured to receive thecontrol data 116, determine a first command datum as a function of thecontrol data 116, and transmit the first command datum to the firstactuator 124. In some cases, first actuator 124 may be configured toreceive first command datum from flight actuator 120. In a case wherefirst actuator 124 fails to receive first command datum from flightactuator 120, the first actuator 124 may contingently determine a firstcommand datum, for instance as a function of control data 116 and adistributed control algorithm. In some cases, at least an actuator maybe configured to automatically perform a reversion, for instance bydetermining a command datum using a distributed control algorithm, whenan absence of input command datum is detected. As described above, insome embodiments at least an actuator may determine its own commanddatum according to sensor data 116. Alternatively or additionally, insome other embodiments, at least an actuator may determine secondcommand datum for a second actuator according to sensor data 116.

Still referring to FIG. 1, in some embodiments, first actuator 124 maybe additionally configured to: determine a second command datum as afunction of control data 116 and distributed control algorithm andtransmit the second command datum to a second actuator 132 communicativewith the first actuator 124. In some cases, second actuator 132 may beconfigured to: receive second command datum and actuate a second controlelement 136 according to the second command datum. Second actuator 132may include any actuator described in this disclosure. Second controlelement 136 may include any control element described in thisdisclosure.

Within continued reference to FIG. 1, at least an actuator may includeany computing device as described in this disclosure, including withoutlimitation a microcontroller, microprocessor, digital signal processor(DSP) and/or system on a chip (SoC) as described in this disclosure.Computing device may include, be included in, and/or communicate with amobile device such as a mobile telephone or smartphone. At least anactuator may include a single computing device operating independently,or may include two or more computing device operating in concert, inparallel, sequentially or the like; two or more computing devices may beincluded together in a single computing device or in two or morecomputing devices. At least an actuator may interface or communicatewith one or more additional computing devices as described below infurther detail via a network interface device. Network interface devicemay be utilized for connecting at least an actuator to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device. Atleast an actuator may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. At least an actuator may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. At least an actuator may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. At least an actuator may beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofactuator and/or computing device.

At least an actuator may be designed and/or configured to perform anymethod, method step, or sequence of method steps in any embodimentdescribed in this disclosure, in any order and with any degree ofrepetition. For instance, at least an actuator may be configured toperform a single step or sequence repeatedly until a desired orcommanded outcome is achieved; repetition of a step or a sequence ofsteps may be performed iteratively and/or recursively using outputs ofprevious repetitions as inputs to subsequent repetitions, aggregatinginputs and/or outputs of repetitions to produce an aggregate result,reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. At least an actuator mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing. Actuator, as well as any other componentpresent within disclosed systems, as well as any other components orcombination of components may be connected to a controller area network(CAN) which may interconnect all components for signal transmission andreception.

Still referring to FIG. 1, according to some embodiments, at least anactuator or flight controller 120 may be configured to perform a votingalgorithm. In some cases, performing voting algorithm includesdetermining that at least a sensor of a plurality of sensors 104 is anallowed sensor 128. Voting algorithm may also be configured to translatepilot input 112 into commands suitable for movement of control surfacesmechanically coupled to an aircraft. For example, and withoutlimitation, there may be more than one allowed sensor from plurality ofsensors 104 with associated control data 116 determined to be active andadmissible. Active and/or admissible control data 116 may be received byvoting algorithm. Voting algorithm may combine active and/or admissiblecontrol data 116 to generate and/or output a command datum; combiningmay include without limitation any form of mathematical aggregation,such as a sum, a weighted sum, a product, a weighted product, atriangular norm such as a minimum, bounded product, algebraic product,drastic product, or the like, a triangular co-norm such as a maximum,bounded sum, algebraic sum, drastic sum, or the like, an average such asan arithmetic and/or geometric mean, or the like. One of ordinary skillin the art, after reviewing the entirety of this disclosure, wouldappreciate that averaging (finding the mean) of a plurality datums ofcontroller data 116 from a plurality of allowed sensors 104 is only oneexample of mathematical or other operations suitable to take all “votes”into account when generating a command datum. An allowed sensor mayinclude a sensor that has not been banned by a flight controller 120 orat least an actuator. One of ordinary skill in the art would appreciate,after reviewing the entirety of this disclosure, that any number offlight controllers can perform any number of the herein disclosed stepsin combination with other computing devices or systems, and performthese calculations relating to any number of components, banning andunbanning any component in system 100. Flight controller 120 or at leastan actuator determines if at least a sensor of plurality of sensors 104is an allowed sensor by determining if the at least a sensor'scorresponding control datum 116 is an active datum. An “active datum,”as used in this disclosure, is to a datum which is received bycommunicatively coupled device within a predetermined and expected timelimit. For example and without limitation, flight controller 120 or atleast an actuator may calculate when at least a sensor may be expectedto transmit a control datum 116; if that control datum 116 arrivesoutside of an expected time limit (i.e., time range), then the controldatum 116 may be determined to not be an active datum. If flightcontroller 120 or at least an actuator receives a control datum 116within an expected time range, then the control datum 116 may bedetermined to be an active datum. Filtering inactive datums may be asafeguard against old or stale data, wherein stale data may be outdated,for instance in view of more recent pilot inputs 112. Flight controller120 or at least an actuator may perform a voting algorithm in order todetermine if a control datum 116 is an admissible datum. An “admissibledatum,” as used in this disclosure, is an element of data which iswithin a predetermined and/or expected admissible range. An “admissiblerange,” as used in this disclosure, is control data 116 value that ifused to actuate a control surface would result in a movement of thecontrol surface, which is admissible. For instance in some non-limitingembodiments an admissible movement is a movement that is considered safein view of environmental conditions, aircraft conditions, missionconsiderations, and/or aircraft power considerations. For example, andwithout limitation, pilot input 112 may be embodied by a pilot moving aninceptor stick to the right, a plurality of sensors 104 senses a pilotinput 112 and transmit control data 116, including a plurality ofcontrol datums; a control datum may be transmitted to and determined tobe an active datum by flight controller 120 or at least an actuator.Flight controller 120 or at least an actuator may further receiveinformation from onboard and offboard sensors that measure environmentalconditions, such as without limitation airspeed, angle of attack, andair density, as well as aircraft conditions like battery level. Flightcontroller 120 or at least an actuator may perform voting algorithmconsistent with any voting algorithm described herein.

With continued reference to FIG. 1, flight controller 120 or at least analgorithm is configured to ban the at least a sensor of plurality ofsensors 104 that transmitted a control datum of control data 116determined to not be an active datum. A “ban,” as used in thisdisclosure, is an exclusion of one or more datums of controller data116, for example without limitation by flight controller or at least anactuator. For example, and without limitation, flight controller 120 orat least an actuator may ban a bad sensor of plurality of sensors 104that does not transmit a control datum of control data 116 within a timelimit, thereby determining that data from the bad sensor to be nottrustworthy and not accurately representative of a pilot input 112.Thresholds relating to voting algorithm and sensor filtering aredescribed in detail in this disclosure, for instance with reference toFIGS. 2-3. Similarly, flight controller 120 or at least an actuator maybe configured to ban a bad sensor 104 transmitting a control datum ofcontrol data 116, which has been determined not to be an admissibledatum. For example, and without limitation, flight controller 120 or atleast an actuator may determine a control datum 116 is notrepresentative of an admissible controls surface movement, such aswithout limitation a movement that correlates to an admissible range offlight maneuvers given a certain engine power availability and airdensity. Voting algorithm may utilize one or more machine-learningprocesses consistent with the entirety of this disclosure, and inparticular with reference to FIG. 5.

With continued reference to FIG. 1, flight controller 120 or at least anactuator may be configured to generate, as a function of control data116, a command datum correlated to pilot input 112. Command datum may bean electrical signal consistent with any electrical signal described inthis disclosure. Command datum may be an electrical signal generated byflight controller 120 or at least an actuator using control datums thatare both active and admissible. According to some embodiments, commanddatum may be a mean of a plurality of command datums, active datums,admissible datums, or the like, for example being derived from anynumber of allowed sensors of plurality of sensors 104. For example, andwithout limitation, at least a sensor of plurality of sensors 104 mayinclude ten independent sensors detecting pilot input 112. Continuingwith the example, two of the ten independent sensors may be determinedto transmit non-active datums and are thus banned; three additionalsensors may be determined to transmit non-admissible datums and are thusbanned. In this example, the remaining five allowed sensors may be usedto perform one or more mathematical operations on their control datumsto output at least a command datum that represents a collective value insome way; hence, in this example, each sensor can be said to have“voted” on what value command datum should be. In some cases, commanddatum may be a command to move an aileron mechanically coupled toaircraft consistent with this disclosure. In some cases, command datummay be a command to a propulsor mechanically coupled to an electricaircraft, like an electric motor, propeller, combustion engine, or thelike.

Referring now to FIG. 2, an exemplary embodiment of a voting algorithm200 is presented in block diagram form. Voting algorithm 200 may receivedata from component 204A-D. Component 204A-D may include sensors, sensorsuites, flight controllers, computing devices, electronic component, orother aircraft component as described herein. For example, and withoutlimitation, component 204A-D, may include four independent sensors, eachof which may be at least a sensor 104. In some cases, component 204A mayindicate, as an electrical signal or element of data, it is ban status208. A “ban status,” as used this disclosure, is a status of a componentwithin system 100; ban status 208 may be ‘banned’ or ‘unbanned’. Ifcomponent 204A is banned, its vote may not be counted, as it may not bea sensor whose data is considered usable for generation of a commanddatum. In some cases, a system that is banned may be unbanned overmultiple iterations of banning algorithm, as disclosed in thisdisclosure. For example, and without limitation, component 204A may notbe banned, or in other words, a control datum of control data 116transmitted by component 204A may be taken into consideration by votingalgorithm 200. Unbanned component 204A may then include an active datumstatus 212. If command datum 116 is transmitted from an unbanned sensor,in the ongoing example component 204A, and is transmitted within apredetermined time limit, time range, speed, or in-line with another orcombination of other temporal considerations, active datum status 212may be determined. Active datum status 212 may include whether or not acontrol datum was transmitted to flight controller 120 or at least anactuator in a temporally appropriate manner. If so, control datum 116may be determined to include an admissible datum status 216. In somecases, admissible datum status 216 may include whether control datum 116is an admissible datum, or that it correlates to an admissible controlsurface movement. One of ordinary skill in the art would appreciate,after reviewing the entirety of this disclosure, that determination ofactive datum status 212 and admissible datum status 216 may notnecessarily be sequential; that any determinations may be made in anyorder; that the determinations may be made separately; that samecomputing devices may be used in the determinations of each statusrelating to a single component; or, that multiple computing systems maybe used in the determination of statuses relating to multiplecomponents.

Continuing to refer to FIG. 2, voting algorithm 200, after determiningthat control datums relating to allowed components are active datums (atactive datum status 212) and admissible datums (at admissible datumstatus 216), transmits control datums to voter module 220. Voter module220 may be performed on any computing device or component thereof asdescribed in this disclosure, including without limitation at least anactuator or a flight controller 120. Voter module 220 may be performedusing any of an analog circuit, digital circuit, combinatorial logiccircuit, sequential logic circuit and/or another circuit suitable, orthe like. Voter module 220 may perform any of method steps, operations,calculations, or other manipulations of command datums relating toallowed components 204A-D. Voter module 220, for example, may receivefour control datums relating, for example to a change in an aircraft'syaw, as described in this disclosure. Voter module 220 may averagecontrol datums and output the average as output datum 224. Output datum224 would in this case be a mean of all control datums associated witheach of allowed components 204A-D. Output datum 224 may, in some cases,be same or similar to command datum. Output datum 224 may be transmittedto any portion of an electric aircraft, including but not limited tocomputing devices, flight controllers, signal conditioners, actuators,propulsors, control surfaces, or the like.

Referring now to FIG. 3, an exemplary banning algorithm 300 is presentedin block diagram form. Banning algorithm may include current ban status304. Current ban status 304 may include information or one or moreelements of data referring to a component's current status as determinedby one or more flight controllers 120 and/or at least an actuator.Current ban status 304 may include a binary value, such as withoutlimitation 1 or 0, indicating currently banned or not currently banned.Current ban status 304 may include an electrical signal representingbanned or unbanned status. If current ban status 304 indicates componentis currently banned, currently banned process 308 may be initiated.Tolerance datum 316 may be determined by flight controller 120 or atleast an actuator as a range of values corresponding to a previouslyvoted on value, such as output datum 224 or command datum. Tolerancedatum 316 may be iteratively determined, mathematically manipulated inmultiple iterations of a loop, such as in a computer code; or tolerancedatum 316 may be input by one or more personnel. Tolerance datum 316 mayindicate a range of values acceptable in currently banned process 308.For example, and without limitation, if a currently banned componenttransmits an electrical signal that does not fall within a previouslyvoted on tolerance datum 316, a tolerance count re-zero 324 may beinitiated. Tolerance count re-zero 324 may be a state wherein aniterative process of unbanning a banned component is brought back tozero. If a currently banned component transmits a datum included intolerance datum 316, then tolerance count increment 320 may beinitiated. Tolerance count increment 320 may increase a tolerance count,where a currently banned sensor may be unbanned by provided data thatcoincides with previously voted on datums. If tolerance count increment320 increases past a tolerance threshold 328, then the unban command 332may be initiated. According to some embodiments, tolerance threshold 328may include a debounce. In some cases. tolerance threshold 328 may haveunits of iterations or time. For example, and without limitation,tolerance threshold 328 may be five iterations, wherein an iterativeprocess of reading a currently banned component's data must be withintolerance datum 316 five times consecutively before the component isunbanned by unban command 332. Unban command 332 may be transmitted toflight controller 120 or at least an actuator, or directly to the newlyunbanned component, like at least a sensor 104.

Continuing to refer to FIG. 3, if currently banned status 304 indicatesthe component is currently unbanned, then currently unbanned process 312may be initiated. If currently unbanned process 312 is initiated, thenrecent ban status 336 may be determined. Recent ban status 336 indicatesif component was voted out in a previous iteration of signaltransmission, i.e., the component was not transmitting active andadmissible data consistent with the entirety of this disclosure. Ifcurrently unbanned component transmits data out of tolerance withpreviously voted on data, vote out count increment 340 may be initiated.Vote out count increment 340 may indicate an increase in vote out count,the vote out count, if raised above vote out threshold 348, ban command342 is initiated. If currently unbanned component has a recently bannedstatus 336 indicating it has not been recently voted out, then vote outcount decrement 344 may be initiated. Vote out count decrement 344decreases vote out count, further removing a currently unbannedcomponent from being banned by ban command 342, indicating that thecurrently unbanned component is transmitting usable and accurate data.Currently banned process 308 and currently unbanned process 312 may berepeatedly performed before any components are banned or unbanned,performed in periodic intervals, performed in a specific order,performed simultaneously, performed on same components at a same time,performed on all components simultaneously, among others.

According to some embodiments, at least an actuator may include aninverter that may be configured to control an electric motor. Anelectric motor may, in turn, actuate a flight component for example acontrol surface and/or a propulsor. At least an inverter may provideelectrical power to stator. Stator that at least an inverter powers maybe configurable. For the purposes of this disclosure, configurable maymean that a user, a machine, a computer, or a combination thereof, maychange or adjust stator, and more accurately, modular winding sets thatat least an inverter provides electrical power to. One of ordinary skillin the art would appreciate virtually limitless combination of invertersand modular winding sets that may be used in power assembly and furtherin stator assembly. At least an inverter may be disposed in or on atleast a portion of stator assembly or motor. Exemplary embodiments ofinverters are illustrated below for exemplary purposes; there may be anynumber of inverters and corresponding windings, including withoutlimitation six inverters and six corresponding windings. An “inverter,”as used in this this disclosure, is a power electronic device orcircuitry that changes direct current (DC) to alternative current (AC).An inverter (also called a power inverter) may be entirely electronic ormay include at least a mechanism (such as a rotary apparatus) andelectronic circuitry. In some embodiments, static inverters may not usemoving parts in conversion process. Inverters may not produce any poweritself; rather, inverters may convert power produced by a DC powersource. Inverters may often be used in electrical power applicationswhere high currents and voltages are present; circuits that perform asimilar function, as inverters, for electronic signals, havingrelatively low currents and potentials, may be referred to asoscillators. In some cases, circuits that perform opposite function toan inverter, converting AC to DC, may be referred to as rectifiers.Further description related to inverts and their use with electricalmotors used on electric VTOL aircraft is disclosed within U.S. patentapplication Ser. Nos. 17/144,304 and 17/197,427 entitled “METHODS ANDSYSTEMS FOR A FRACTIONAL CONCENTRATED STATOR CONFIGURED FOR USE INELECTRIC AIRCRAFT MOTOR” and “SYSTEM AND METHOD FOR FLIGHT CONTROL INELECTRIC AIRCRAFT” by C. Lin et al. T. Richter et al., respectively,both of which are incorporated herein by reference in their entirety.

Referring now to FIG. 4, an exemplary embodiment of an aircraft 400 isillustrated. Aircraft 400 may include an electrically powered aircraft.In some embodiments, electrically powered aircraft may be an electricvertical takeoff and landing (eVTOL) aircraft. Electric aircraft may becapable of rotor-based cruising flight, rotor-based takeoff, rotor-basedlanding, fixed-wing cruising flight, airplane-style takeoff,airplane-style landing, and/or any combination thereof “Rotor-basedflight,” as described in this disclosure, is where the aircraftgenerated lift and propulsion by way of one or more powered rotorscoupled with an engine, such as a quadcopter, multi-rotor helicopter, orother vehicle that maintains its lift primarily using downward thrustingpropulsors. “Fixed-wing flight,” as described in this disclosure, iswhere the aircraft is capable of flight using wings and/or foils thatgenerate lift caused by the aircraft's forward airspeed and the shape ofthe wings and/or foils, such as airplane-style flight.

Still referring to FIG. 4, aircraft 400 may include a fuselage 404. Asused in this disclosure a “fuselage” is the main body of an aircraft, orin other words, the entirety of the aircraft except for the cockpit,nose, wings, empennage, nacelles, any and all control surfaces, andgenerally contains an aircraft's payload. Fuselage 404 may comprisestructural elements that physically support the shape and structure ofan aircraft. Structural elements may take a plurality of forms, alone orin combination with other types. Structural elements may vary dependingon the construction type of aircraft and specifically, the fuselage.Fuselage 404 may comprise a truss structure. A truss structure may beused with a lightweight aircraft and may include welded aluminum tubetrusses. A truss, as used herein, is an assembly of beams that create arigid structure, often in combinations of triangles to createthree-dimensional shapes. A truss structure may alternatively comprisetitanium construction in place of aluminum tubes, or a combinationthereof. In some embodiments, structural elements may comprise aluminumtubes and/or titanium beams. In an embodiment, and without limitation,structural elements may include an aircraft skin. Aircraft skin may belayered over the body shape constructed by trusses. Aircraft skin maycomprise a plurality of materials such as aluminum, fiberglass, and/orcarbon fiber, the latter of which will be addressed in greater detaillater in this paper.

Still referring to FIG. 4, aircraft 400 may include a plurality ofactuators 408. Actuator 408 may include any actuator described in thisdisclosure, for instance in reference to FIGS. 1-3. In an embodiment,actuator 408 may be mechanically coupled to an aircraft. As used herein,a person of ordinary skill in the art would understand “mechanicallycoupled” to mean that at least a portion of a device, component, orcircuit is connected to at least a portion of the aircraft via amechanical coupling. Said mechanical coupling can include, for example,rigid coupling, such as beam coupling, bellows coupling, bushed pincoupling, constant velocity, split-muff coupling, diaphragm coupling,disc coupling, donut coupling, elastic coupling, flexible coupling,fluid coupling, gear coupling, grid coupling, Hirth joints, hydrodynamiccoupling, jaw coupling, magnetic coupling, Oldham coupling, sleevecoupling, tapered shaft lock, twin spring coupling, rag joint coupling,universal joints, or any combination thereof. As used in this disclosurean “aircraft” is vehicle that may fly. As a non-limiting example,aircraft may include airplanes, helicopters, airships, blimps, gliders,paramotors, and the like thereof. In an embodiment, mechanical couplingmay be used to connect the ends of adjacent parts and/or objects of anelectric aircraft. Further, in an embodiment, mechanical coupling may beused to join two pieces of rotating electric aircraft components.

With continued reference to FIG. 4, a plurality of actuators 408 may beconfigured to produce a torque. As used in this disclosure a “torque” isa measure of force that causes an object to rotate about an axis in adirection. For example, and without limitation, torque may rotate anaileron and/or rudder to generate a force that may adjust and/or affectaltitude, airspeed velocity, groundspeed velocity, direction duringflight, and/or thrust. For example, plurality of actuators 408 mayinclude a component used to produce a torque that affects aircrafts'roll and pitch, such as without limitation one or more ailerons. An“aileron,” as used in this disclosure, is a hinged surface which formpart of the trailing edge of a wing in a fixed wing aircraft, and whichmay be moved via mechanical means such as without limitationservomotors, mechanical linkages, or the like. As a further example,plurality of actuators 408 may include a rudder, which may include,without limitation, a segmented rudder that produces a torque about avertical axis. Additionally or alternatively, plurality of actuators 408may include other flight control surfaces such as propulsors, rotatingflight controls, or any other structural features which can adjustmovement of aircraft 400. Plurality of actuators 408 may include one ormore rotors, turbines, ducted fans, paddle wheels, and/or othercomponents configured to propel a vehicle through a fluid mediumincluding, but not limited to air.

Still referring to FIG. 4, plurality of actuators 408 may include atleast a propulsor component. As used in this disclosure a “propulsorcomponent” is a component and/or device used to propel a craft byexerting force on a fluid medium, which may include a gaseous mediumsuch as air or a liquid medium such as water. In an embodiment, when apropulsor twists and pulls air behind it, it may, at the same time, pushan aircraft forward with an amount of force and/or thrust. More airpulled behind an aircraft results in greater thrust with which theaircraft is pushed forward. Propulsor component may include any deviceor component that consumes electrical power on demand to propel anelectric aircraft in a direction or other vehicle while on ground orin-flight. In an embodiment, propulsor component may include a pullercomponent. As used in this disclosure a “puller component” is acomponent that pulls and/or tows an aircraft through a medium. As anon-limiting example, puller component may include a flight componentsuch as a puller propeller, a puller motor, a puller propulsor, and thelike. Additionally, or alternatively, puller component may include aplurality of puller flight components. In another embodiment, propulsorcomponent may include a pusher component. As used in this disclosure a“pusher component” is a component that pushes and/or thrusts an aircraftthrough a medium. As a non-limiting example, pusher component mayinclude a pusher component such as a pusher propeller, a pusher motor, apusher propulsor, and the like. Additionally, or alternatively, pusherflight component may include a plurality of pusher flight components.

In another embodiment, and still referring to FIG. 4, propulsor mayinclude a propeller, a blade, or any combination of the two. A propellermay function to convert rotary motion from an engine or other powersource into a swirling slipstream which may push the propeller forwardsor backwards. Propulsor may include a rotating power-driven hub, towhich several radial airfoil-section blades may be attached, such thatan entire whole assembly rotates about a longitudinal axis. As anon-limiting example, blade pitch of propellers may be fixed at a fixedangle, manually variable to a few set positions, automatically variable(e.g. a “constant-speed” type), and/or any combination thereof asdescribed further in this disclosure. As used in this disclosure a“fixed angle” is an angle that is secured and/or substantially unmovablefrom an attachment point. For example, and without limitation, a fixedangle may be an angle of 2.2° inward and/or 1.7° forward. As a furthernon-limiting example, a fixed angle may be an angle of 3.6° outwardand/or 2.7° backward. In an embodiment, propellers for an aircraft maybe designed to be fixed to their hub at an angle similar to the threadon a screw makes an angle to the shaft; this angle may be referred to asa pitch or pitch angle which may determine a speed of forward movementas the blade rotates. Additionally or alternatively, propulsor componentmay be configured having a variable pitch angle. As used in thisdisclosure a “variable pitch angle” is an angle that may be moved and/orrotated. For example, and without limitation, propulsor component may beangled at a first angle of 3.3° inward, wherein propulsor component maybe rotated and/or shifted to a second angle of 1.7° outward.

Still referring to FIG. 4, propulsor may include a thrust element whichmay be integrated into the propulsor. Thrust element may include,without limitation, a device using moving or rotating foils, such as oneor more rotors, an airscrew or propeller, a set of airscrews orpropellers such as contra-rotating propellers, a moving or flappingwing, or the like. Further, a thrust element, for example, can includewithout limitation a marine propeller or screw, an impeller, a turbine,a pump-jet, a paddle or paddle-based device, or the like.

With continued reference to FIG. 4, plurality of actuators 408 mayinclude power sources, control links to one or more elements, fuses,and/or mechanical couplings used to drive and/or control any otherflight component. Plurality of actuators 408 may include a motor thatoperates to move one or more flight control components and/or one ormore control surfaces, to drive one or more propulsors, or the like. Amotor may be driven by direct current (DC) electric power and mayinclude, without limitation, brushless DC electric motors, switchedreluctance motors, induction motors, or any combination thereof.Alternatively or additionally, a motor may be driven by an inverter. Amotor may also include electronic speed controllers, inverters, or othercomponents for regulating motor speed, rotation direction, and/ordynamic braking.

Still referring to FIG. 4, plurality of actuators 408 may include anenergy source. An energy source may include, for example, a generator, aphotovoltaic device, a fuel cell such as a hydrogen fuel cell, directmethanol fuel cell, and/or solid oxide fuel cell, an electric energystorage device (e.g. a capacitor, an inductor, and/or a battery). Anenergy source may also include a battery cell, or a plurality of batterycells connected in series into a module and each module connected inseries or in parallel with other modules. Configuration of an energysource containing connected modules may be designed to meet an energy orpower requirement and may be designed to fit within a designatedfootprint in an electric aircraft in which system may be incorporated.

In an embodiment, and still referring to FIG. 4, an energy source may beused to provide a steady supply of electrical power to a load over aflight by an electric aircraft 400. For example, energy source may becapable of providing sufficient power for “cruising” and otherrelatively low-energy phases of flight. An energy source may also becapable of providing electrical power for some higher-power phases offlight as well, particularly when the energy source is at a high SOC, asmay be the case for instance during takeoff In an embodiment, energysource may include an emergency power unit which may be capable ofproviding sufficient electrical power for auxiliary loads includingwithout limitation, lighting, navigation, communications, de-icing,steering or other systems requiring power or energy. Further, energysource may be capable of providing sufficient power for controlleddescent and landing protocols, including, without limitation, hoveringdescent or runway landing. As used herein the energy source may havehigh power density where electrical power an energy source can usefullyproduce per unit of volume and/or mass is relatively high. As used inthis disclosure, “electrical power” is a rate of electrical energy perunit time. An energy source may include a device for which power thatmay be produced per unit of volume and/or mass has been optimized, forinstance at an expense of maximal total specific energy density or powercapacity. Non-limiting examples of items that may be used as at least anenergy source include batteries used for starting applications includingLi ion batteries which may include NCA, NMC, Lithium iron phosphate(LiFePO4) and Lithium Manganese Oxide (LMO) batteries, which may bemixed with another cathode chemistry to provide more specific power ifthe application requires Li metal batteries, which have a lithium metalanode that provides high power on demand, Li ion batteries that have asilicon or titanite anode, energy source may be used, in an embodiment,to provide electrical power to an electric aircraft or drone, such as anelectric aircraft vehicle, during moments requiring high rates of poweroutput, including without limitation takeoff, landing, thermal de-icingand situations requiring greater power output for reasons of stability,such as high turbulence situations, as described in further detailbelow. A battery may include, without limitation a battery using nickelbased chemistries such as nickel cadmium or nickel metal hydride, abattery using lithium ion battery chemistries such as a nickel cobaltaluminum (NCA), nickel manganese cobalt (NMC), lithium iron phosphate(LiFePO4), lithium cobalt oxide (LCO), and/or lithium manganese oxide(LMO), a battery using lithium polymer technology, lead-based batteriessuch as without limitation lead acid batteries, metal-air batteries, orany other suitable battery. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various devices ofcomponents that may be used as an energy source.

Still referring to FIG. 4, an energy source may include a plurality ofenergy sources, referred to herein as a module of energy sources. Modulemay include batteries connected in parallel or in series or a pluralityof modules connected either in series or in parallel designed to satisfyboth power and energy requirements. Connecting batteries in series mayincrease a potential of at least an energy source which may provide morepower on demand. High potential batteries may require cell matching whenhigh peak load is needed. As more cells are connected in strings, theremay exist a possibility of one cell failing which may increaseresistance in module and reduce overall power output as voltage of themodule may decrease as a result of that failing cell. Connectingbatteries in parallel may increase total current capacity by decreasingtotal resistance, and it also may increase overall amp-hour capacity.Overall energy and power outputs of at least an energy source may bebased on individual battery cell performance or an extrapolation basedon a measurement of at least an electrical parameter. In an embodimentwhere energy source includes a plurality of battery cells, overall poweroutput capacity may be dependent on electrical parameters of eachindividual cell. If one cell experiences high self-discharge duringdemand, power drawn from at least an energy source may be decreased toavoid damage to a weakest cell. Energy source may further include,without limitation, wiring, conduit, housing, cooling system and batterymanagement system. Persons skilled in the art will be aware, afterreviewing the entirety of this disclosure, of many different componentsof an energy source. Exemplary energy sources are disclosed in detail inU.S. patent application Ser. Nos. 16/848,157 and 16/048,140 bothentitled “SYSTEM AND METHOD FOR HIGH ENERGY DENSITY BATTERY MODULE” byS. Donovan et al., which are incorporated in their entirety herein byreference.

Still referring to FIG. 4, according to some embodiments, an energysource may include an emergency power unit (EPU) (i.e., auxiliary powerunit). As used in this disclosure an “emergency power unit” is an energysource as described herein that is configured to power an essentialsystem for a critical function in an emergency, for instance withoutlimitation when another energy source has failed, is depleted, or isotherwise unavailable. Exemplary non-limiting essential systems includenavigation systems, such as MFD, GPS, VOR receiver or directional gyro,and other essential flight components, such as propulsors.

Still referring to FIG. 4, another exemplary actuator may includelanding gear. Landing gear may be used for take-off and/or landing/Landing gear may be used to contact ground while aircraft 400 is not inflight. Exemplary landing gear is disclosed in detail in U.S. patentapplication Ser. No. 17/196,619 entitled “SYSTEM FOR ROLLING LANDINGGEAR” by R. Griffin et al., which is incorporated in its entirety hereinby reference.

Still referring to FIG. 4, aircraft 400 may include a pilot control 412,including without limitation, a hover control, a thrust control, aninceptor stick, a cyclic, and/or a collective control. As used in thisdisclosure a “collective control” is a mechanical control of an aircraftthat allows a pilot to adjust and/or control the pitch angle of theplurality of actuators 408. For example and without limitation,collective control may alter and/or adjust the pitch angle of all of themain rotor blades collectively. For example, and without limitationpilot control 412 may include a yoke control. As used in this disclosurea “yoke control” is a mechanical control of an aircraft to control thepitch and/or roll. For example and without limitation, yoke control mayalter and/or adjust the roll angle of aircraft 400 as a function ofcontrolling and/or maneuvering ailerons. In an embodiment, pilot control412 may include one or more foot-brakes, control sticks, pedals,throttle levels, and the like thereof. In another embodiment, andwithout limitation, pilot control 412 may be configured to control aprincipal axis of the aircraft. As used in this disclosure a “principalaxis” is an axis in a body representing one three dimensionalorientations. For example, and without limitation, principal axis ormore yaw, pitch, and/or roll axis. Principal axis may include a yawaxis. As used in this disclosure a “yaw axis” is an axis that isdirected towards the bottom of the aircraft, perpendicular to the wings.For example, and without limitation, a positive yawing motion mayinclude adjusting and/or shifting the nose of aircraft 400 to the right.Principal axis may include a pitch axis. As used in this disclosure a“pitch axis” is an axis that is directed towards the right laterallyextending wing of the aircraft. For example, and without limitation, apositive pitching motion may include adjusting and/or shifting the noseof aircraft 400 upwards. Principal axis may include a roll axis. As usedin this disclosure a “roll axis” is an axis that is directedlongitudinally towards the nose of the aircraft, parallel to thefuselage. For example, and without limitation, a positive rolling motionmay include lifting the left and lowering the right wing concurrently.

Still referring to FIG. 4, pilot control 412 may be configured to modifya variable pitch angle. For example, and without limitation, pilotcontrol 412 may adjust one or more angles of attack of a propeller. Asused in this disclosure an “angle of attack” is an angle between thechord of the propeller and the relative wind. For example, and withoutlimitation angle of attack may include a propeller blade angled 3.2°. Inan embodiment, pilot control 412 may modify the variable pitch anglefrom a first angle of 2.61° to a second angle of 3.72°. Additionally oralternatively, pilot control 412 may be configured to translate a pilotdesired torque for flight component 108. For example, and withoutlimitation, pilot control 412 may translate that a pilot's desiredtorque for a propeller be 150 lb. ft. of torque. As a furthernon-limiting example, pilot control 412 may introduce a pilot's desiredtorque for a propulsor to be 280 lb. ft. of torque. Additionaldisclosure related to pilot control 412 may be found in U.S. patentapplication Ser. Nos. 17/001,845 and 16/829,206 both of which areentitled “A HOVER AND THRUST CONTROL ASSEMBLY FOR DUAL-MODE AIRCRAFT” byC. Spiegel et al., which are incorporated in their entirety herein byreference.

Still referring to FIG. 4, aircraft 400 may include a loading system. Aloading system may include a system configured to load an aircraft ofeither cargo or personnel. For instance, some exemplary loading systemsmay include a swing nose, which is configured to swing the nose ofaircraft 400 of the way thereby allowing direct access to a cargo baylocated behind the nose. A notable exemplary swing nose aircraft isBoeing 747. Additional disclosure related to loading systems can befound in U.S. patent application Ser. No. 17/137,584 entitled “SYSTEMAND METHOD FOR LOADING AND SECURING PAYLOAD IN AN AIRCRAFT” by R.Griffin et al., entirety of which in incorporated herein by reference.

Still referring to FIG. 4, aircraft 400 may include a sensor 416. Sensor416 may include any sensor described in this disclosure, for instance inreference to FIGS. 1-3. Sensor 416 may be configured to sense acharacteristic of pilot control 412. Sensor may be a device, module,and/or subsystem, utilizing any hardware, software, and/or anycombination thereof to sense a characteristic and/or changes thereof, inan instant environment, for instance without limitation a pilot control412, which the sensor is proximal to or otherwise in a sensedcommunication with, and transmit information associated with thecharacteristic, for instance without limitation digitized data. Sensor416 may be mechanically and/or communicatively coupled to aircraft 400,including, for instance, to at least a pilot control 412. Sensor 416 maybe configured to sense a characteristic associated with at least a pilotcontrol 412. An environmental sensor may include without limitation oneor more sensors used to detect ambient temperature, barometric pressure,and/or air velocity, one or more motion sensors which may includewithout limitation gyroscopes, accelerometers, inertial measurement unit(IMU), and/or magnetic sensors, one or more humidity sensors, one ormore oxygen sensors, or the like. Additionally or alternatively, sensor416 may include at least a geospatial sensor. Sensor 416 may be locatedinside an aircraft; and/or be included in and/or attached to at least aportion of the aircraft. Sensor may include one or more proximitysensors, displacement sensors, vibration sensors, and the like thereof.Sensor may be used to monitor the status of aircraft 400 for bothcritical and non-critical functions. Sensor may be incorporated intovehicle or aircraft or be remote.

Still referring to FIG. 4, in some embodiments, sensor 416 may beconfigured to sense a characteristic associated with any pilot controldescribed in this disclosure. Non-limiting examples of a sensor 416 mayinclude an inertial measurement unit (IMU), an accelerometer, agyroscope, a proximity sensor, a pressure sensor, a light sensor, apitot tube, an air speed sensor, a position sensor, a speed sensor, aswitch, a thermometer, a strain gauge, an acoustic sensor, and anelectrical sensor. In some cases, sensor 416 may sense a characteristicas an analog measurement, for instance, yielding a continuously variableelectrical potential indicative of the sensed characteristic. In thesecases, sensor 416 may additionally comprise an analog to digitalconverter (ADC) as well as any additionally circuitry, such as withoutlimitation a Whetstone bridge, an amplifier, a filter, and the like. Forinstance, in some cases, sensor 416 may comprise a strain gageconfigured to determine loading of one or flight components, forinstance landing gear. Strain gage may be included within a circuitcomprising a Whetstone bridge, an amplified, and a bandpass filter toprovide an analog strain measurement signal having a high signal tonoise ratio, which characterizes strain on a landing gear member. An ADCmay then digitize analog signal produces a digital signal that can thenbe transmitted other systems within aircraft 400, for instance withoutlimitation a computing system, a pilot display, and a memory component.Alternatively or additionally, sensor 416 may sense a characteristic ofa pilot control 412 digitally. For instance in some embodiments, sensor416 may sense a characteristic through a digital means or digitize asensed signal natively. In some cases, for example, sensor 416 mayinclude a rotational encoder and be configured to sense a rotationalposition of a pilot control; in this case, the rotational encoderdigitally may sense rotational “clicks” by any known method, such aswithout limitation magnetically, optically, and the like.

Still referring to FIG. 4, electric aircraft 400 may include at least amotor 424, which may be mounted on a structural feature of the aircraft.Design of motor 424 may enable it to be installed external to structuralmember (such as a boom, nacelle, or fuselage) for easy maintenanceaccess and to minimize accessibility requirements for the structure;this may improve structural efficiency by requiring fewer large holes inthe mounting area. In some embodiments, motor 424 may include two mainholes in top and bottom of mounting area to access bearing cartridge.Further, a structural feature may include a component of electricaircraft 400. For example, and without limitation structural feature maybe any portion of a vehicle incorporating motor 424, including anyvehicle as described in this disclosure. As a further non-limitingexample, a structural feature may include without limitation a wing, aspar, an outrigger, a fuselage, or any portion thereof; persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of many possible features that may function as at least astructural feature. At least a structural feature may be constructed ofany suitable material or combination of materials, including withoutlimitation metal such as aluminum, titanium, steel, or the like, polymermaterials or composites, fiberglass, carbon fiber, wood, or any othersuitable material. As a non-limiting example, at least a structuralfeature may be constructed from additively manufactured polymer materialwith a carbon fiber exterior; aluminum parts or other elements may beenclosed for structural strength, or for purposes of supporting, forinstance, vibration, torque or shear stresses imposed by at leastpropulsor 408. Persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various materials, combinations ofmaterials, and/or constructions techniques.

Still referring to FIG. 4, electric aircraft 400 may include a verticaltakeoff and landing aircraft (eVTOL). As used herein, a verticaltake-off and landing (eVTOL) aircraft is one that can hover, take off,and land vertically. An eVTOL, as used herein, is an electricallypowered aircraft typically using an energy source, of a plurality ofenergy sources to power the aircraft. In order to optimize the power andenergy necessary to propel the aircraft. eVTOL may be capable ofrotor-based cruising flight, rotor-based takeoff, rotor-based landing,fixed-wing cruising flight, airplane-style takeoff, airplane-stylelanding, and/or any combination thereof. Rotor-based flight, asdescribed herein, is where the aircraft generated lift and propulsion byway of one or more powered rotors coupled with an engine, such as a“quad copter,” multi-rotor helicopter, or other vehicle that maintainsits lift primarily using downward thrusting propulsors. Fixed-wingflight, as described herein, is where the aircraft is capable of flightusing wings and/or foils that generate life caused by the aircraft'sforward airspeed and the shape of the wings and/or foils, such asairplane-style flight.

With continued reference to FIG. 4, a number of aerodynamic forces mayact upon the electric aircraft 400 during flight. Forces acting onelectric aircraft 400 during flight may include, without limitation,thrust, the forward force produced by the rotating element of theelectric aircraft 400 and acts parallel to the longitudinal axis.Another force acting upon electric aircraft 400 may be, withoutlimitation, drag, which may be defined as a rearward retarding forcewhich is caused by disruption of airflow by any protruding surface ofthe electric aircraft 400 such as, without limitation, the wing, rotor,and fuselage. Drag may oppose thrust and acts rearward parallel to therelative wind. A further force acting upon electric aircraft 400 mayinclude, without limitation, weight, which may include a combined loadof the electric aircraft 400 itself, crew, baggage, and/or fuel. Weightmay pull electric aircraft 400 downward due to the force of gravity. Anadditional force acting on electric aircraft 400 may include, withoutlimitation, lift, which may act to oppose the downward force of weightand may be produced by the dynamic effect of air acting on the airfoiland/or downward thrust from the propulsor 408 of the electric aircraft.Lift generated by the airfoil may depend on speed of airflow, density ofair, total area of an airfoil and/or segment thereof, and/or an angle ofattack between air and the airfoil. For example, and without limitation,electric aircraft 400 are designed to be as lightweight as possible.Reducing the weight of the aircraft and designing to reduce the numberof components is essential to optimize the weight. To save energy, itmay be useful to reduce weight of components of electric aircraft 400,including without limitation propulsors and/or propulsion assemblies. Inan embodiment, motor 424 may eliminate need for many external structuralfeatures that otherwise might be needed to join one component to anothercomponent. Motor 424 may also increase energy efficiency by enabling alower physical propulsor profile, reducing drag and/or wind resistance.This may also increase durability by lessening the extent to which dragand/or wind resistance add to forces acting on electric aircraft 400and/or propulsors.

Now referring to FIG. 5, an exemplary embodiment 500 of a flightcontroller 504 is illustrated. As used in this disclosure a “flightcontroller” is a computing device of a plurality of computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and flight instruction. Flight controller 504 may includeand/or communicate with any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Further, flight controller 504may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. In embodiments, flight controller 504 may be installed in anaircraft, may control the aircraft remotely, and/or may include anelement installed in the aircraft and a remote element in communicationtherewith.

In an embodiment, and still referring to FIG. 5, flight controller 504may include a signal transformation component 508. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 508 maybe configured to perform one or more operations such as preprocessing,lexical analysis, parsing, semantic analysis, and the like thereof. Inan embodiment, and without limitation, signal transformation component508 may include one or more analog-to-digital convertors that transforma first signal of an analog signal to a second signal of a digitalsignal. For example, and without limitation, an analog-to-digitalconverter may convert an analog input signal to a 10-bit binary digitalrepresentation of that signal. In another embodiment, signaltransformation component 508 may include transforming one or morelow-level languages such as, but not limited to, machine languagesand/or assembly languages. For example, and without limitation, signaltransformation component 508 may include transforming a binary languagesignal to an assembly language signal. In an embodiment, and withoutlimitation, signal transformation component 508 may include transformingone or more high-level languages and/or formal languages such as but notlimited to alphabets, strings, and/or languages. For example, andwithout limitation, high-level languages may include one or more systemlanguages, scripting languages, domain-specific languages, visuallanguages, esoteric languages, and the like thereof. As a furthernon-limiting example, high-level languages may include one or morealgebraic formula languages, business data languages, string and listlanguages, object-oriented languages, and the like thereof

Still referring to FIG. 5, signal transformation component 508 may beconfigured to optimize an intermediate representation 512. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 508 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 508 may optimizeintermediate representation 512 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 508 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 508 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller 504. For example, and without limitation, nativemachine language may include one or more binary and/or numericallanguages.

In an embodiment, and without limitation, signal transformationcomponent 508 may include transform one or more inputs and outputs as afunction of an error correction code. An error correction code, alsoknown as error correcting code (ECC), is an encoding of a message or lotof data using redundant information, permitting recovery of corrupteddata. An ECC may include a block code, in which information is encodedon fixed-size packets and/or blocks of data elements such as symbols ofpredetermined size, bits, or the like. Reed-Solomon coding, in whichmessage symbols within a symbol set having q symbols are encoded ascoefficients of a polynomial of degree less than or equal to a naturalnumber k, over a finite field F with q elements; strings so encoded havea minimum hamming distance of k+1, and permit correction of (q−k−1)/2erroneous symbols. Block code may alternatively or additionally beimplemented using Golay coding, also known as binary Golay coding,Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-checkcoding, and/or Hamming codes. An ECC may alternatively or additionallybe based on a convolutional code.

In an embodiment, and still referring to FIG. 5, flight controller 504may include a reconfigurable hardware platform 516. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform 516 may be reconfiguredto enact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learningprocesses.

Still referring to FIG. 5, reconfigurable hardware platform 516 mayinclude a logic component 520. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 520 may include any suitable processor, such aswithout limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 520 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 520 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating-point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 520 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 520 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 512. Logiccomponent 520 may be configured to fetch and/or retrieve the instructionfrom a memory cache, wherein a “memory cache,” as used in thisdisclosure, is a stored instruction set on flight controller 504. Logiccomponent 520 may be configured to decode the instruction retrieved fromthe memory cache to opcodes and/or operands. Logic component 520 may beconfigured to execute the instruction on intermediate representation 512and/or output language. For example, and without limitation, logiccomponent 520 may be configured to execute an addition operation onintermediate representation 512 and/or output language.

In an embodiment, and without limitation, logic component 520 may beconfigured to calculate a flight element 524. As used in this disclosurea “flight element” is an element of datum denoting a relative status ofaircraft 500. For example, and without limitation, flight element 524may denote one or more torques, thrusts, airspeed velocities, forces,altitudes, groundspeed velocities, directions during flight, directionsfacing, forces, orientations, and the like thereof. For example, andwithout limitation, flight element 524 may denote that aircraft 500 iscruising at an altitude and/or with a sufficient magnitude of forwardthrust. As a further non-limiting example, flight status may denote that500 is building thrust and/or groundspeed velocity in preparation for atakeoff. As a further non-limiting example, flight element 524 maydenote that aircraft 500 is following a flight path accurately and/orsufficiently.

Still referring to FIG. 5, flight controller 504 may include a chipsetcomponent 528. As used in this disclosure a “chipset component” is acomponent that manages data flow. In an embodiment, and withoutlimitation, chipset component 528 may include a northbridge data flowpath, wherein the northbridge dataflow path may manage data flow fromlogic component 520 to a high-speed device and/or component, such as aRAM, graphics controller, and the like thereof. In another embodiment,and without limitation, chipset component 528 may include a southbridgedata flow path, wherein the southbridge dataflow path may manage dataflow from logic component 520 to lower-speed peripheral buses, such as aperipheral component interconnect (PCI), industry standard architecture(ICA), and the like thereof. In an embodiment, and without limitation,southbridge data flow path may include managing data flow betweenperipheral connections such as ethernet, USB, audio devices, and thelike thereof. Additionally or alternatively, chipset component 528 maymanage data flow between logic component 520, memory cache, and a flightcomponent 532. As used in this disclosure a “flight component” is aportion of an aircraft that can be moved or adjusted to affect one ormore flight elements. For example, flight component 532 may include acomponent used to affect the aircrafts' roll and pitch which maycomprise one or more ailerons. As a further example, flight component532 may include a rudder to control yaw of an aircraft. In anembodiment, chipset component 528 may be configured to communicate witha plurality of flight components as a function of flight element 524.For example, and without limitation, chipset component 528 may transmitto an aircraft rotor to reduce torque of a first lift propulsor andincrease the forward thrust produced by a pusher component to perform aflight maneuver.

In an embodiment, and still referring to FIG. 5, flight controller 504may be configured generate an autonomous function. As used in thisdisclosure an “autonomous function” is a mode and/or function of flightcontroller 504 that controls aircraft 500 automatically. For example,and without limitation, autonomous function may perform one or moreaircraft maneuvers, take offs, landings, altitude adjustments, flightleveling adjustments, turns, climbs, and/or descents. As a furthernon-limiting example, autonomous function may adjust one or moreairspeed velocities, thrusts, torques, and/or groundspeed velocities. Asa further non-limiting example, autonomous function may perform one ormore flight path corrections and/or flight path modifications as afunction of flight element 524. In an embodiment, autonomous functionmay include one or more modes of autonomy such as, but not limited to,autonomous mode, semi-autonomous mode, and/or non-autonomous mode. Asused in this disclosure “autonomous mode” is a mode that automaticallyadjusts and/or controls aircraft 500 and/or the maneuvers of aircraft500 in its entirety.

In an embodiment, and still referring to FIG. 5, flight controller 504may generate autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 524 and a pilot signal536 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot signal” is an element of datum representing one or more functionsa pilot is controlling and/or adjusting. For example, pilot signal 536may denote that a pilot is controlling and/or maneuvering ailerons,wherein the pilot is not in control of the rudders and/or propulsors. Inan embodiment, pilot signal 536 may include an implicit signal and/or anexplicit signal. For example, and without limitation, pilot signal 536may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 536 may include an explicitsignal directing flight controller 504 to control and/or maintain aportion of aircraft 500, a portion of the flight plan, the entireaircraft, and/or the entire flight plan. As a further non-limitingexample, pilot signal 536 may include an implicit signal, wherein flightcontroller 504 detects a lack of control such as by a malfunction,torque alteration, flight path deviation, and the like thereof. In anembodiment, and without limitation, pilot signal 536 may include one ormore explicit signals to reduce torque, and/or one or more implicitsignals that torque may be reduced due to reduction of airspeedvelocity. In an embodiment, and without limitation, pilot signal 536 mayinclude one or more local and/or global signals. For example, andwithout limitation, pilot signal 536 may include a local signal that istransmitted by a pilot and/or crew member. As a further non-limitingexample, pilot signal 536 may include a global signal that istransmitted by air traffic control and/or one or more remote users thatare in communication with the pilot of aircraft 500. In an embodiment,pilot signal 536 may be received as a function of a tri-state bus and/ormultiplexor that denotes an explicit pilot signal should be transmittedprior to any implicit or global pilot signal.

Still referring to FIG. 5, autonomous machine-learning model may includeone or more autonomous machine-learning processes such as supervised,unsupervised, or reinforcement machine-learning processes that flightcontroller 504 and/or a remote device may or may not use in thegeneration of autonomous function. As used in this disclosure “remotedevice” is an external device to flight controller 504. Additionally oralternatively, autonomous machine-learning model may include one or moreautonomous machine-learning processes that a field-programmable gatearray (FPGA) may or may not use in the generation of autonomousfunction. Autonomous machine-learning process may include, withoutlimitation machine learning processes such as simple linear regression,multiple linear regression, polynomial regression, support vectorregression, ridge regression, lasso regression, elasticnet regression,decision tree regression, random forest regression, logistic regression,logistic classification, K-nearest neighbors, support vector machines,kernel support vector machines, naive bayes, decision treeclassification, random forest classification, K-means clustering,hierarchical clustering, dimensionality reduction, principal componentanalysis, linear discriminant analysis, kernel principal componentanalysis, Q-learning, State Action Reward State Action (SARSA), Deep-Qnetwork, Markov decision processes, Deep Deterministic Policy Gradient(DDPG), or the like thereof.

In an embodiment, and still referring to FIG. 5, autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller 504 may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 5, flight controller 504 may receive autonomousmachine-learning model from a remote device and/or FPGA that utilizesone or more autonomous machine learning processes, wherein a remotedevice and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller 504. Remote device and/orFPGA may transmit a signal, bit, datum, or parameter to flightcontroller 504 that at least relates to autonomous function.Additionally or alternatively, the remote device and/or FPGA may providean updated machine-learning model. For example, and without limitation,an updated machine-learning model may be comprised of a firmware update,a software update, an autonomous machine-learning process correction,and the like thereof. As a non-limiting example a software update mayincorporate a new simulation data that relates to a modified flightelement. Additionally or alternatively, the updated machine learningmodel may be transmitted to the remote device and/or FPGA, wherein theremote device and/or FPGA may replace the autonomous machine-learningmodel with the updated machine-learning model and generate theautonomous function as a function of the flight element, pilot signal,and/or simulation data using the updated machine-learning model. Theupdated machine-learning model may be transmitted by the remote deviceand/or FPGA and received by flight controller 504 as a software update,firmware update, or corrected habit machine-learning model. For example,and without limitation autonomous machine learning model may utilize aneural net machine-learning process, wherein the updatedmachine-learning model may incorporate a gradient boostingmachine-learning process.

Still referring to FIG. 5, flight controller 504 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Further, flight controller may communicate withone or more additional devices as described below in further detail viaa network interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 5, flight controller 504may include, but is not limited to, for example, a cluster of flightcontrollers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controller504 may include one or more flight controllers dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Flight controller 504 may be configured to distribute one or morecomputing tasks as described below across a plurality of flightcontrollers, which may operate in parallel, in series, redundantly, orin any other manner used for distribution of tasks or memory betweencomputing devices. For example, and without limitation, flightcontroller 504 may implement a control algorithm to distribute and/orcommand the plurality of flight controllers. As used in this disclosurea “control algorithm” is a finite sequence of well-defined computerimplementable instructions that may determine the flight component ofthe plurality of flight components to be adjusted. For example, andwithout limitation, control algorithm may include one or more algorithmsthat reduce and/or prevent aviation asymmetry. As a further non-limitingexample, control algorithms may include one or more models generated asa function of a software including, but not limited to Simulink byMathWorks, Natick, Mass., USA. In an embodiment, and without limitation,control algorithm may be configured to generate an auto-code, wherein an“auto-code,” is used herein, is a code and/or algorithm that isgenerated as a function of the one or more models and/or software's. Inanother embodiment, control algorithm may be configured to produce asegmented control algorithm. As used in this disclosure a “segmentedcontrol algorithm” is control algorithm that has been separated and/orparsed into discrete sections. For example, and without limitation,segmented control algorithm may parse control algorithm into two or moresegments, wherein each segment of control algorithm may be performed byone or more flight controllers operating on distinct flight components.

In an embodiment, and still referring to FIG. 5, control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with the segments ofthe segmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of flight component 532. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive the one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furthercomprises separating a plurality of signal codes across the plurality offlight controllers. For example, and without limitation the plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, the plurality of flight controllers may include achain path, wherein a “chain path,” as used herein, is a linearcommunication path comprising a hierarchy that data may flow through. Inan embodiment, and without limitation, the plurality of flightcontrollers may include an all-channel path, wherein an “all-channelpath,” as used herein, is a communication path that is not restricted toa particular direction. For example, and without limitation, data may betransmitted upward, downward, laterally, and the like thereof. In anembodiment, and without limitation, the plurality of flight controllersmay include one or more neural networks that assign a weighted value toa transmitted datum. For example, and without limitation, a weightedvalue may be assigned as a function of one or more signals denoting thata flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 5, the plurality of flight controllers mayinclude a master bus controller. As used in this disclosure a “masterbus controller” is one or more devices and/or components that areconnected to a bus to initiate a direct memory access transaction,wherein a bus is one or more terminals in a bus architecture. Master buscontroller may communicate using synchronous and/or asynchronous buscontrol protocols. In an embodiment, master bus controller may includeflight controller 504. In another embodiment, master bus controller mayinclude one or more universal asynchronous receiver-transmitters (UART).For example, and without limitation, master bus controller may includeone or more bus architectures that allow a bus to initiate a directmemory access transaction from one or more buses in the busarchitectures. As a further non-limiting example, master bus controllermay include one or more peripheral devices and/or components tocommunicate with another peripheral device and/or component and/or themaster bus controller. In an embodiment, master bus controller may beconfigured to perform bus arbitration. As used in this disclosure “busarbitration” is method and/or scheme to prevent multiple buses fromattempting to communicate with and/or connect to master bus controller.For example and without limitation, bus arbitration may include one ormore schemes such as a small computer interface system, wherein a smallcomputer interface system is a set of standards for physical connectingand transferring data between peripheral devices and master buscontroller by defining commands, protocols, electrical, optical, and/orlogical interfaces. In an embodiment, master bus controller may receiveintermediate representation 512 and/or output language from logiccomponent 520, wherein output language may include one or moreanalog-to-digital conversions, low bit rate transmissions, messageencryptions, digital signals, binary signals, logic signals, analogsignals, and the like thereof described above in detail.

Still referring to FIG. 5, master bus controller may communicate with aslave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 5, control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of the master buscontrol. As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated between theplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 5, flight controller 504 may also be implementedusing a “shared nothing” architecture in which data is cached at theworker, in an embodiment, this may enable scalability of aircraft and/orcomputing device. Flight controller 504 may include a distributer flightcontroller. As used in this disclosure a “distributer flight controller”is a component that adjusts and/or controls a plurality of flightcomponents as a function of a plurality of flight controllers. Forexample, distributer flight controller may include a flight controllerthat communicates with a plurality of additional flight controllersand/or clusters of flight controllers. In an embodiment, distributedflight control may include one or more neural networks. For example,neural network also known as an artificial neural network, is a networkof “nodes,” or data structures having one or more inputs, one or moreoutputs, and a function determining outputs based on inputs. Such nodesmay be organized in a network, such as without limitation aconvolutional neural network, including an input layer of nodes, one ormore intermediate layers, and an output layer of nodes. Connectionsbetween nodes may be created via the process of “training” the network,in which elements from a training dataset are applied to the inputnodes, a suitable training algorithm (such as Levenberg-Marquardt,conjugate gradient, simulated annealing, or other algorithms) is thenused to adjust the connections and weights between nodes in adjacentlayers of the neural network to produce the desired values at the outputnodes. This process is sometimes referred to as deep learning.

Still referring to FIG. 5, a node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above. In anembodiment, and without limitation, a neural network may receivesemantic units as inputs and output vectors representing such semanticunits according to weights w_(i) that are derived using machine-learningprocesses as described in this disclosure.

Still referring to FIG. 5, flight controller may include asub-controller 540. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller 504 may be and/orinclude a distributed flight controller made up of one or moresub-controllers. For example, and without limitation, sub-controller 540may include any controllers and/or components thereof that are similarto distributed flight controller and/or flight controller as describedabove. Sub-controller 540 may include any component of any flightcontroller as described above. Sub-controller 540 may be implemented inany manner suitable for implementation of a flight controller asdescribed above. As a further non-limiting example, sub-controller 540may include one or more processors, logic components and/or computingdevices capable of receiving, processing, and/or transmitting dataacross the distributed flight controller as described above. As afurther non-limiting example, sub-controller 540 may include acontroller that receives a signal from a first flight controller and/orfirst distributed flight controller component and transmits the signalto a plurality of additional sub-controllers and/or flight components.

Still referring to FIG. 5, flight controller may include a co-controller544. As used in this disclosure a “co-controller” is a controller and/orcomponent that joins flight controller 504 as components and/or nodes ofa distributer flight controller as described above. For example, andwithout limitation, co-controller 544 may include one or morecontrollers and/or components that are similar to flight controller 504.As a further non-limiting example, co-controller 544 may include anycontroller and/or component that joins flight controller 504 todistributer flight controller. As a further non-limiting example,co-controller 544 may include one or more processors, logic componentsand/or computing devices capable of receiving, processing, and/ortransmitting data to and/or from flight controller 504 to distributedflight control system. Co-controller 544 may include any component ofany flight controller as described above. Co-controller 544 may beimplemented in any manner suitable for implementation of a flightcontroller as described above.

In an embodiment, and with continued reference to FIG. 5, flightcontroller 504 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller 504 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Flight controller may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Referring now to FIG. 6, an exemplary embodiment of a machine-learningmodule 600 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 604 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 608 given data provided as inputs 612;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 6, “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 604 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 604 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 604 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 604 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 604 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 604 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data604 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 6,training data 604 may include one or more elements that are notcategorized; that is, training data 604 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 604 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 604 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 604 used by machine-learning module 600 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample flight elements and/or pilot signals may be inputs, wherein anoutput may be an autonomous function.

Further referring to FIG. 6, training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 616. Training data classifier 616 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 600 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 604. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 416 may classify elements of training data tosub-categories of flight elements such as torques, forces, thrusts,directions, and the like thereof.

Still referring to FIG. 6, machine-learning module 600 may be configuredto perform a lazy-learning process 620 and/or protocol, which mayalternatively be referred to as a “lazy loading” or “call-when-needed”process and/or protocol, may be a process whereby machine learning isconducted upon receipt of an input to be converted to an output, bycombining the input and training set to derive the algorithm to be usedto produce the output on demand. For instance, an initial set ofsimulations may be performed to cover an initial heuristic and/or “firstguess” at an output and/or relationship. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data 604. Heuristic may include selecting somenumber of highest-ranking associations and/or training data 604elements. Lazy learning may implement any suitable lazy learningalgorithm, including without limitation a K-nearest neighbors algorithm,a lazy naïve Bayes algorithm, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variouslazy-learning algorithms that may be applied to generate outputs asdescribed in this disclosure, including without limitation lazy learningapplications of machine-learning algorithms as described in furtherdetail below.

Alternatively or additionally, and with continued reference to FIG. 6,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 624. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 624 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 624 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 604set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 6, machine-learning algorithms may include atleast a supervised machine-learning process 628. At least a supervisedmachine-learning process 628, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude flight elements and/or pilot signals as described above asinputs, autonomous functions as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 604. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process628 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 6, machine learning processes may include atleast an unsupervised machine-learning processes 632. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 6, machine-learning module 600 may be designedand configured to create a machine-learning model 624 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 6, machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naive Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring now to FIG. 7, an exemplary method 700 of fly-by-wirereversionary flight control is illustrated by way of a flow diagram. Atstep 705, a plurality of sensors sense control data associated with apilot control. Sensors may include any sensors described in thisdisclosure, including for instance in reference to FIGS. 1-6. Controldata may include any control data described in this disclosure,including for instance in reference to FIGS. 1-6. Pilot control mayinclude any pilot control described in this disclosure, including forinstance in reference to FIGS. 1-6.

Continuing with reference to FIG. 7, at step 710, plurality of sensorstransmit control data. Transmission may include any transmission orcommunication methods described in this disclosure, including inreference to FIGS. 1-6.

Continuing with reference to FIG. 7, at step 715, a first actuator,communicative with plurality of sensors, receives control data. Firstactuator may include any actuator described in this disclosure,including in reference to FIGS. 1-6.

Continuing with reference to FIG. 7, at step 720, first actuatordetermines a first command datum as a function of control data and adistributed control algorithm. First command datum may include anycommand datum described in this disclosure, including for instance inreference to FIGS. 1-6. A distributed control algorithm may include anyalgorithm described in this disclosure, including for instance inreference to FIGS. 1-6. In some embodiments, distributed controlalgorithm may be configured to filter control data. Filtering mayinclude any data filtering method described in this disclosure. In somecases, filtering control data may comprise voting. Voting may includeany voting method described in this disclosure. In some embodiments,distributed control algorithm may be configured to find an average oftwo or more control datums of control data. In some embodiments,distributed control algorithm may be configured to find a differencebetween two or more control datums of control data.

Continuing with reference to FIG. 7, at step 725, first actuatoractuates a first control element according to first command datum.Actuating a first control element may include any method for moving,controlling, adjusting, configuring, transforming, translating,rotating, and the like described in this disclosure, including inreference to FIGS. 1-6. First control element may include any controlelement described in this disclosure, including in reference to FIGS.1-6. In some cases, first control element may include a propulsor.

Still referring to FIG. 7, in some embodiments method 700 mayadditionally include additional steps. In an additional step, a flightcontroller, communicative with plurality of sensors and first actuator,may receive control data. In another additional step, flight controllermay determine first command datum as a function of control data. Inanother additional step, flight controller may transmit first commanddatum to first actuator. In another additional step, first actuator mayreceive first command datum from flight controller. Flight controllermay include any flight controller described in this disclosure,including in reference to FIGS. 1-6. In still more embodiments, method700 may include yet another additional step, wherein first actuatorcontingently determines first command datum when unable to receive thefirst command datum from flight controller. For instance, in some cases,first actuator may under normal conditions receive first command datumfrom flight controller; but when the first actuator is unable, forwhatever reason, to receive the command datum from the flightcontroller, the first actuator determines the command datum.

Still referring to FIG. 7, in some embodiments, method may additionallyinclude first actuator determining a second command datum as a functionof control data and distributed control algorithm; and first actuatortransmitting the second command datum to a second actuator communicativewith the first actuator. In some cases, method may additionally includesecond actuator receiving second command datum; and second actuating asecond control element according to the second command datum. Secondactuator may include any actuator described in this application,including for instance in reference to FIGS. 1-6. Second control elementmay include any control element described in this application, includingfor instance in reference to FIGS. 1-6.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 8 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 800 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 800 includes a processor 804 and a memory808 that communicate with each other, and with other components, via abus 812. Bus 812 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 804 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 804 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 804 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 808 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 816 (BIOS), including basic routines that help totransfer information between elements within computer system 800, suchas during start-up, may be stored in memory 808. Memory 808 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 820 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 808 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 800 may also include a storage device 824. Examples of astorage device (e.g., storage device 824) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 824 may be connected to bus 812 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1384 (FIREWIRE), and anycombinations thereof. In one example, storage device 824 (or one or morecomponents thereof) may be removably interfaced with computer system 800(e.g., via an external port connector (not shown)). Particularly,storage device 824 and an associated machine-readable medium 828 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 800. In one example, software 820 may reside, completelyor partially, within machine-readable medium 828. In another example,software 820 may reside, completely or partially, within processor 804.

Computer system 800 may also include an input device 832. In oneexample, a user of computer system 800 may enter commands and/or otherinformation into computer system 800 via input device 832. Examples ofan input device 832 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 832may be interfaced to bus 812 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 812, and any combinations thereof. Input device 832 mayinclude a touch screen interface that may be a part of or separate fromdisplay 836, discussed further below. Input device 832 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 800 via storage device 824 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 840. A network interfacedevice, such as network interface device 840, may be utilized forconnecting computer system 800 to one or more of a variety of networks,such as network 844, and one or more remote devices 848 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 844,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 820,etc.) may be communicated to and/or from computer system 800 via networkinterface device 840.

Computer system 800 may further include a video display adapter 842 forcommunicating a displayable image to a display device, such as displaydevice 836. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 842 and display device 836 may be utilized incombination with processor 804 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 800 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 812 via a peripheral interface 956. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for fly-by-wire reversionary flightcontrol, the system comprising: an electric aircraft, wherein theelectric aircraft is configured to include: a first control element; afirst actuator coupled to the first control element, wherein the firstactuator is configured to: receive a control data; determine a firstcommand datum as a function of the control data and a distributedcontrol algorithm; and actuate a first control element as a function ofthe first command datum.
 2. The system of claim 1, wherein the firstactuator is further configured to receive the control data from a remotepilot control.
 3. The system of claim 1, wherein the first actuator isfurther configured to include a computing device.
 4. The system of claim1, wherein the first actuator is further configured to include a motor.5. The system of claim 1, further comprising: a flight controllercommunicatively coupled to the first actuator, wherein the flightcontroller is configured to: receive the control data; determine thefirst command datum as a function of the control data; and transmit thefirst command datum to the first actuator.
 6. The system of claim 5,wherein the first actuator is further configured to: determine that thefirst actuator is not receiving the first command datum from the flightcontroller; and determine the first command datum as a function of thecontrol data. The system of claim 1, wherein the distributed controlalgorithm comprises filtering the control data.
 8. The system of claim7, wherein filtering the control data comprises a voting process.
 9. Thesystem of claim 1, wherein the first actuator is further configured to:determine a second command datum as a function of the control data andthe distributed control algorithm; and transmit the second command datumto a second actuator communicative with the first actuator.
 10. Thesystem of claim 10, wherein the second actuator is configured to:receive the second command datum; and actuate a second control elementas a function of the second command datum.
 11. A method of fly-by-wirereversionary flight control for an electric aircraft, the methodcomprising: receiving, at a first actuator coupled to a first controlelement, a control data; determining, at the first actuator coupled tothe first control element, a first command datum as a function of thecontrol data and a distributed control algorithm; and actuating, at thefirst actuator coupled to the first control element, a first controlelement as a function of the first command datum.
 12. The method ofclaim 11, wherein the first actuator is further configured to receivethe control data from a remote pilot control.
 13. The method of claim11, wherein the first actuator is further configured to include acomputing device.
 14. The method of claim 11, wherein the first actuatoris further configured to include a motor.
 15. The method of claim 11,further comprising: receiving, at a flight controller c communicativelycoupled to the first actuator, the control data; determining, at theflight controller, the first command datum as a function of the controldata; and transmitting, at the flight controller, the first commanddatum to the first actuator.
 16. The method of claim 15, furthercomprising: determining, at the first actuator, that the first actuatoris not receiving the first command datum from the flight controller; andcontingently determining, at the first actuator, the first command datumwhen unable to receive the first command datum from the flightcontroller.
 17. The method of claim 11, wherein the distributed controlalgorithm is configured to filter the control data.
 18. The method ofclaim 17, wherein filtering the control data comprises performing avoting process.
 19. The method of claim 11, further comprising:determining, at the first actuator, a second command datum as a functionof the control data and the distributed control algorithm; andtransmitting, at the first actuator, the second command datum to asecond actuator communicative with the first actuator.
 20. The method ofclaim 19, further comprising: receiving, at the second actuator, thesecond command datum; and actuating, at the second actuator, a secondcontrol element as a function of the second command datum.